OpenRaiser/Intern-Atlas huggingface dataset:
The Microsoft 2017 Conversational Speech Recognition System
Venue: arXiv cs.CL 2017
Year: 2017

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set.
Knock Knock, Who's There? Membership Inference on Aggregate Location Data
Venue: arXiv cs.CR 2017
Year: 2017

Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses. In this paper, we present the first study on the feasibility of membership inference attacks on aggregate location time-series. We introduce a game-based definition of the adversarial task, and cast it as a classification problem where machine learning can be used to distinguish whether or not a target user is part of the aggregates. We empirically evaluate the power of these attacks on both raw and differentially private aggregates using two mobility datasets. We find that membership inference is a serious privacy threat, and show how its effectiveness depends on the adversary's prior knowledge, the characteristics of the underlying location data, as well as the number of users and the timeframe on which aggregation is performed. Although differentially private mechanisms can indeed reduce the extent of the attacks, they also yield a significant loss in utility. Moreover, a strategic adversary mimicking the behavior of the defense mechanism can greatly limit the protection they provide. Overall, our work presents a novel methodology geared to evaluate membership inference on aggregate location data in real-world settings and can be used by providers to assess the quality of privacy protection before data release or by regulators to detect violations.
Recognizing Involuntary Actions from 3D Skeleton Data Using Body States
Venue: arXiv cs.CV 2017
Year: 2017

Human action recognition has been one of the most active fields of research in computer vision for last years. Two dimensional action recognition methods are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method of action recognition which uses temporal 3D skeletal Kinect data. This method introduces the definition of body states and then every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods, with recognition rate of 88.64% for eight different actions and up to 96.18% for classifying fall actions.
Comparative Benchmarking of Causal Discovery Techniques
Venue: arXiv cs.AI 2017
Year: 2017

In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) allowing both cycles and latent variables, along with experimental results comparing them from three perspectives: (a) structural accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual inference. For (b) and (c) we train causal Bayesian networks with structures as predicted by each causal discovery technique to carry out counterfactual or standard predictive inference. We compare causal algorithms on two pub- licly available and one simulated datasets having different sample sizes: small, medium and large. Experiments show that structural accuracy of a technique does not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther, surveyed structure learning algorithms do not perform well in terms of structural accuracy in case of datasets having large number of variables.
Partial-Duplex Amplify-and-Forward Relaying: Spectral Efficiency Analysis under Self-Interference
Venue: arXiv cs.IT 2017
Year: 2017

We propose a novel mode of operation for Amplify-and-Forward relays in which the spectra of the relay input and output signals partially overlap. This partial-duplex relaying mode encompasses half-duplex and full-duplex as particular cases. By viewing the partial-duplex relay as a bandwidth-preserving Linear Periodic Time-Varying system, an analysis of the spectral efficiency in the presence of self-interference is developed. In contrast with previous works, self-interference is regarded as a useful information-bearing component rather than simply assimilated to noise. This approach reveals that previous results regarding the impact of self-interference on (full-duplex) relay performance are overly pessimistic. Based on a frequency-domain interpretation of the effect of self-interference, a number of suboptimal decoding architectures at the destination node are also discussed. It is found that the partial-duplex relaying mode may provide an attractive tradeoff between spectral efficiency and receiver complexity.
Employing Weak Annotations for Medical Image Analysis Problems
Venue: arXiv cs.CV 2017
Year: 2017

To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort. However, when this concept is ported to the medical imaging domain, reading expertise will have a direct impact on the annotation accuracy. In this study, we examine the impact of expertise and the amount of available annotations on the accuracy outcome of a liver segmentation problem in an abdominal computed tomography (CT) image database. In controlled experiments, we study this impact for different types of weak annotations. To address the decrease in accuracy associated with lower expertise, we propose a method for outlier correction making use of a weakly labelled atlas. Using this approach, we demonstrate that weak annotations subject to high error rates can achieve a similarly high accuracy as state-of-the-art multi-atlas segmentation approaches relying on a large amount of expert manual segmentations. Annotations of this nature can realistically be obtained from a non-expert crowd and can potentially enable crowdsourcing of weak annotation tasks for medical image analysis.
Detecting Location Fraud in Indoor Mobile Crowdsensing
Venue: arXiv cs.CR 2017
Year: 2017

Mobile crowdsensing allows a large number of mobile devices to measure phenomena of common interests and form a body of knowledge about natural and social environments. In order to get location annotations for indoor mobile crowdsensing, reference tags are usually deployed which are susceptible to tampering and compromises by attackers. In this work, we consider three types of location-related attacks including tag forgery, tag misplacement, and tag removal. Different detection algorithms are proposed to deal with these attacks. First, we introduce location-dependent fingerprints as supplementary information for better location identification. A truth discovery algorithm is then proposed to detect falsified data. Moreover, visiting patterns are utilized for the detection of tag misplacement and removal. Experiments on both crowdsensed and emulated dataset show that the proposed algorithms can detect all three types of attacks with high accuracy.
An Intelligent Cloud Storage Gateway for Medical Imaging
Venue: arXiv cs.DC 2017
Year: 2017

Historically, medical imaging repositories have been supported by indoor infrastructures. However, the amount of diagnostic imaging procedures has continuously increased over the last decades, imposing several challenges associated with the storage volume, data redundancy and availability. Cloud platforms are focused on delivering hardware and software services over the Internet, becoming an appealing solution for repository outsourcing. Although this option may bring financial and technological benefits, it also presents new challenges. In medical imaging scenarios, communication latency is a critical issue that still hinders the adoption of this paradigm. This paper proposes an intelligent Cloud storage gateway that optimizes data access times. This is achieved through a new cache architecture that combines static rules and pattern recognition for eviction and prefetching. The evaluation results, obtained through simulations over a real-world dataset, show that cache hit ratios can reach around 80%, leading reductions of image retrieval times by over 60%. The combined use of eviction and prefetching policies pro- posed can significantly reduce communication latency, even when using a small cache in comparison to the total size of the repository. Apart from the performance gains, the proposed system is capable of adjusting to specific workflows of different institutions.
Explainable Recommendation: Theory and Applications
Venue: arXiv cs.IR 2017
Year: 2017

Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems. For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1. Data Explainability: We propose Localized Matrix Factorization (LMF) framework based Bordered Block Diagonal Form (BBDF) matrices, and further applied this technique for parallelized matrix factorization. 2. Model Explainability: We propose Explicit Factor Models (EFM) based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. In this work, we extract product features and user opinions towards different features from large-scale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. 3. Economic Explainability: We propose the Total Surplus Maximization (TSM) framework for personalized recommendation, as well as the model specification in different types of online applications. Based on basic economic concepts, we provide the definitions of utility, cost, and surplus in the application scenario of Web services, and propose the general framework of web total surplus calculation and maximization.
Entirely protecting operating systems against transient errors in space environment
Venue: arXiv cs.OS 2017
Year: 2017

In this article, we propose a mainly-software hardening technique to totally protect unmodified running operating systems on COTS hardware against transient errors in heavily radiation - flooded environment like high altitude space. The technique is currently being implemented in a hypervisor and allows to control the upper layers of the software stack (operating system and applications). The rest of the system, the hypervisor, will be protected by other means, thus resulting in a completely protected system against transient errors. The induced overhead turns around 200% but this is expected to decrease with future improvements.
(Tissue) P Systems with Vesicles of Multisets
Venue: arXiv cs.FL 2017
Year: 2017

We consider tissue P systems working on vesicles of multisets with the very simple operations of insertion, deletion, and substitution of single objects. With the whole multiset being enclosed in a vesicle, sending it to a target cell can be indicated in those simple rules working on the multiset. As derivation modes we consider the sequential mode, where exactly one rule is applied in a derivation step, and the set maximal mode, where in each derivation step a non-extendable set of rules is applied. With the set maximal mode, computational completeness can already be obtained with tissue P systems having a tree structure, whereas tissue P systems even with an arbitrary communication structure are not computationally complete when working in the sequential mode. Adding polarizations (-1, 0, 1 are sufficient) allows for obtaining computational completeness even for tissue P systems working in the sequential mode.
On the Descriptional Complexity of Operations on Semilinear Sets
Venue: arXiv cs.FL 2017
Year: 2017

We investigate the descriptional complexity of operations on semilinear sets. Roughly speaking, a semilinear set is the finite union of linear sets, which are built by constant and period vectors. The interesting parameters of a semilinear set are: (i) the maximal value that appears in the vectors of periods and constants and (ii) the number of such sets of periods and constants necessary to describe the semilinear set under consideration. More precisely, we prove upper bounds on the union, intersection, complementation, and inverse homomorphism. In particular, our result on the complementation upper bound answers an open problem from [G. J. LAVADO, G. PIGHIZZINI, S. SEKI: Operational State Complexity of Parikh Equivalence, 2014].
Multi-layer Analysis of IoT-based Systems
Venue: arXiv cs.SY 2017
Year: 2017

This document provides a theoretical-methodological ground to sustain the idea that the IoT builds the structure of awareness of large-scale infrastructures viewed as techno-social cyber-physical systems, which are special cases of self-developing reflexive-active systems. As the last phrase already indicates, we need to go through a series of explanations before reaching the point of being capable of analyzing the dynamics of IoT-based systems, constituted by physical, information and regulatory layers. We expect through this text to clarify what is the structure of awareness by revisiting the little known Lefebvre's notation. From this standpoint, we can analytically show systemic differences that appears when agents using information about the physical system and/or about the other agents (re)act within the system itself, determining then the actually realized system dynamics. We provide an example of how to carry out this kind of research using the example of smart appliances as a form of stabilizing the grid frequency.
IllusionPIN: Shoulder-Surfing Resistant Authentication Using Hybrid Images
Venue: arXiv cs.CR 2017
Year: 2017

We address the problem of shoulder-surfing attacks on authentication schemes by proposing IllusionPIN (IPIN), a PIN-based authentication method that operates on touchscreen devices. IPIN uses the technique of hybrid images to blend two keypads with different digit orderings in such a way, that the user who is close to the device is seeing one keypad to enter her PIN, while the attacker who is looking at the device from a bigger distance is seeing only the other keypad. The user's keypad is shuffled in every authentication attempt since the attacker may memorize the spatial arrangement of the pressed digits. To reason about the security of IllusionPIN, we developed an algorithm which is based on human visual perception and estimates the minimum distance from which an observer is unable to interpret the keypad of the user. We tested our estimations with 84 simulated shoulder-surfing attacks from 21 different people. None of the attacks was successful against our estimations. In addition, we estimated the minimum distance from which a camera is unable to capture the visual information from the keypad of the user. Based on our analysis, it seems practically almost impossible for a surveillance camera to capture the PIN of a smartphone user when IPIN is in use.
Stacked transfer learning for tropical cyclone intensity prediction
Venue: arXiv cs.LG 2017
Year: 2017

Tropical cyclone wind-intensity prediction is a challenging task considering drastic changes climate patterns over the last few decades. In order to develop robust prediction models, one needs to consider different characteristics of cyclones in terms of spatial and temporal characteristics. Transfer learning incorporates knowledge from a related source dataset to compliment a target datasets especially in cases where there is lack or data. Stacking is a form of ensemble learning focused for improving generalization that has been recently used for transfer learning problems which is referred to as transfer stacking. In this paper, we employ transfer stacking as a means of studying the effects of cyclones whereby we evaluate if cyclones in different geographic locations can be helpful for improving generalization performs. Moreover, we use conventional neural networks for evaluating the effects of duration on cyclones in prediction performance. Therefore, we develop an effective strategy that evaluates the relationships between different types of cyclones through transfer learning and conventional learning methods via neural networks.
Support-Free Hollowing for 3D Printing via Voronoi Diagram of Ellipses
Venue: arXiv cs.CG 2017
Year: 2017

Recent work has demonstrated that the interior material layout of a 3D model can be designed to make a fabricated replica satisfy application-specific demands on its physical properties such as resistance to external loads. A widely used practice to fabricate such models is by layer-based additive manufacturing (AM) or 3D printing, which however suffers from the problem of adding and removing interior supporting structures. In this paper, we present a novel method for generating support-free elliptic hollowing for 3D shapes which can entirely avoid additional supporting structures. To achieve this, we perform the ellipse hollowing in the polygons on parallel section planes and protrude the ellipses of one plane to its neighboring planes. To pack the ellipses in a polygon, we construct the Voronoi diagram of ellipses to efficiently reason the free-space around the ellipses and other geometric features by taking advantage of the available algorithm for the efficient and robust construction of the Voronoi diagram of circles. We demonstrate the effectiveness and feasibility of our proposed method by generating interior designs for and printing various 3D shapes.
Coverage Maximization for a Poisson Field of Drone Cells
Venue: arXiv cs.NI 2017
Year: 2017

The use of drone base stations to provide wireless connectivity for ground terminals is becoming a promising part of future technologies. The design of such aerial networks is however different compared to cellular 2D networks, as antennas from the drones are looking down, and the channel model becomes height-dependent. In this paper, we study the effect of antenna patterns and height-dependent shadowing. We consider a random network topology to capture the effect of dynamic changes of the flying base stations. First we characterize the aggregate interference imposed by the co-channel neighboring drones. Then we derive the link coverage probability between a ground user and its associated drone base station. The result is used to obtain the optimum system parameters in terms of drones antenna beamwidth, density and altitude. We also derive the average LoS probability of the associated drone and show that it is a good approximation and simplification of the coverage probability in low altitudes up to 500 m according to the required signal-to-interference-plus-noise ratio (SINR).
Intra-Cluster Autonomous Coverage Optimization For Dense LTE-A Networks
Venue: arXiv cs.NI 2017
Year: 2017

Self Organizing Networks (SONs) are considered as vital deployments towards upcoming dense cellular networks. From a mobile carrier point of view, continuous coverage optimization is critical for better user perceptions. The majority of SON contributions introduce novel algorithms that optimize specific performance metrics. However, they require extensive processing delays and advanced knowledge of network statistics that may not be available. In this work, a progressive Autonomous Coverage Optimization (ACO) method combined with adaptive cell dimensioning is proposed. The proposed method emphasizes the fact that the effective cell coverage is a variant on actual user distributions. ACO algorithm builds a generic Space-Time virtual coverage map per cell to detect coverage holes in addition to limited or extended coverage conditions. Progressive levels of optimization are followed to timely resolve coverage issues with maintaining optimization stability. Proposed ACO is verified under both simulations and practical deployment in a pilot cluster for a worldwide mobile carrier. Key Performance Indicators show that proposed ACO method significantly enhances system coverage and performance.
A rule based algorithm for detecting negative words in Persian
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we present a novel method for detecting negative words in Persian. We first used an algorithm to an exceptions list which was later modified by hand. We then used the mentioned lists and a Persian polarity corpus in our rule based algorithm to detect negative words.
Upward Partitioned Book Embeddings
Venue: arXiv cs.CG 2017
Year: 2017

We analyze a directed variation of the book embedding problem when the page partition is prespecified and the nodes on the spine must be in topological order (upward book embedding). Given a directed acyclic graph and a partition of its edges into $k$ pages, can we linearly order the vertices such that the drawing is upward (a topological sort) and each page avoids crossings? We prove that the problem is NP-complete for $k\ge 3$, and for $k\ge 4$ even in the special case when each page is a matching. By contrast, the problem can be solved in linear time for $k=2$ pages when pages are restricted to matchings. The problem comes from Jack Edmonds (1997), motivated as a generalization of the map folding problem from computational origami.